• International Journal of Highway Engineering
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• v.17 no.1
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• pp.1-6
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• 2015

PURPOSES : Recently, attempts have been made to evaluate tire-pavement noise based on a measure of Mean Profile Depth (MPD). However, equivalent values of MPD appear to correspond to different levels of tire-pavement noise, which indicates that other factors such as texture wavelength need to be included to improve the accuracy of noise prediction. A single index to represent texture wavelength is proposed in this study. A consistent relationship between tire-pavement noise and texture wavelength on asphalt concrete pavement is observed. METHODS : Profile data and tire-pavement noise data were collected from a number of expressway sections in Korea. In addition, texture wavelength was defined by a Peak Number (PN), which was calculated using profile data. Statistical analysis was performed to find the relationship between the PN and tire-pavement noise. RESULTS : As a result of this study, a linear relationship between PN and tire-pavement noise is observed on asphalt concrete pavement. CONCLUSIONS : Tire-pavement noise on asphalt concrete pavement can be predicted from PN information.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.256-262
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• 2004

Conventionally, profile non-uniformity has been characterized by relying on approximated profile with angle or anisotropy. In this study, a new non-uniformity model for etch profile is presented by applying a discrete wavelet to the image obtained from a scanning electron microscopy (SEM). Prediction models for wavelet-transformed data are then constructed using a back-propagation neural network. The proposed method was applied to the data collected from the etching of tungsten material. Additionally, 7 experiments were conducted to obtain test data. Model performance was evaluated in terms of the average prediction accuracy (APA) and the best prediction accuracy (BPA). To take into account randomness in initial weights, two hundred models were generated for a given set of training factors. Behaviors of the APA and BPA were investigated as a function of training factors, including training tolerance, hidden neuron, initial weight distribution, and two slopes for bipolar sig-moid and linear function. For all variations in training factors, the APA was not consistent with the BPA. The prediction accuracy was optimized using three approaches, the best model based approach, the average model based approach and the combined model based approach. Despite the largest APA of the first approach, its BPA was smallest compared to the other two approaches.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.373-382
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• 2016

In this study, an EKF (Extended Kalman Filter) based database reference navigation using both gravity gradient and terrain data was performed to complement the weakness of using only one type of geophysical DB (Database). Furthermore, a new algorithm which combines the EKF and profile matching was developed to improve the stability and accuracy of the positioning. On the basis of simulations, it was found that the overall navigation performance was improved by the combination of geophysical DBs except the two trajectories in which the divergence of TRN (Terrain Referenced Navigation) occurred. To solve the divergence problem, the profile matching algorithm using the terrain data is combined with the EKF. The results show that all trajectories generate the stable performance with positioning error ranges between 14m to 23m although not all trajectories positioning accuracy is improved. The average positioning error from the combined algorithm for all nine trajectories is about 18 m. For further study, a development of a switching geophysical DB or algorithm between the EKF and the profile matching to improve the navigation performance is suggested.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.6
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• pp.279-285
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• 2017

In general, the power law and logarithmic profile are commonly used as flow vertical velocity profile model. However, since the parameters of profile vary with characteristics of coastal environment, it is necessary to estimate these values from measured data using regression analysis. In this paper, we estimated the power law exponent (n), friction velocity ($u^*$) and roughness length ($z_0$) of logarithmic profile by analyzing measured tidal current data that are averaged at a interval of 30 min. In the results of analysis, power law exponent (n) was estimated to be about 10.75 during flood and about 9.3 during ebb. Meanwhile, $u^*$ of logarithmic profile was estimated to be about 0.084 m/s, 0.105 m/s during flood and ebb, respectively. Also, $z_0$ was estimated to be 0.004 m and 0.006 m, respectively.

• Journal of Preventive Medicine and Public Health
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• v.49 no.3
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• pp.143-152
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• 2016

Objectives: Metabolic syndrome is a cluster of risk factors for type 2 diabetes mellitus and cardiovascular disease. Associations between metabolic syndrome and several types of cancer have recently been documented. Methods: We analyzed the sample cohort data from the Korean National Health Insurance Service from 2002, with a follow-up period extending to 2013. The cohort data included 99 565 individuals who participated in the health examination program and whose data were therefore present in the cohort database. The metabolic risk profile of each participant was assessed based on obesity, high serum glucose and total cholesterol levels, and high blood pressure. The occurrence of cancer was identified using Korean National Health Insurance claims data. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models, adjusting for age group, smoking status, alcohol intake, and regular exercise. Results: A total of 5937 cases of cancer occurred during a mean follow-up period of 10.4 years. In men with a high-risk metabolic profile, the risk of colon cancer was elevated (HR, 1.40; 95% CI, 1.14 to 1.71). In women, a high-risk metabolic profile was associated with a significantly increased risk of gallbladder and biliary tract cancer (HR, 2.05; 95% CI, 1.24 to 3.42). Non-significantly increased risks were observed in men for pharynx, larynx, rectum, and kidney cancer, and in women for colon, liver, breast, and ovarian cancer. Conclusions: The findings of this study support the previously suggested association between metabolic syndrome and the risk of several cancers. A high-risk metabolic profile may be an important risk factor for colon cancer in Korean men and gallbladder and biliary tract cancer in Korean women.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.367-377
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• 2019

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.37-46
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• 2012

In the existing data dissemination methods for mobile P2P networks, the search performance of content that matches the peer profile is very excellent. However, in the search for content that does not match the their profile, additional consideration about case that contents does not match the profile is needed because costs for the query processing will be incurred. To solve these problems, we propose a new data dissemination method for efficient contents search in mobile P2P networks. In the proposed method, peers determine whether they experienced communications by using a timestamp message and then perform data dissemination. We also propose a ranking algorithm to efficiently store dissemination data in a limited memory. The proposed ranking method can reduce query messages by considering the profile matches, the distribution range, and the connectivity to the data distribution peer.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.229-232
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• 2007

In hot strip rolling, the work roll profile is one of the main factors in predicting and correcting the strip profile. Various studies concerning the wear profile and the thermal crown of work roll have been performed, and the results of these studies have shown that the work roll profile must be predicted accurately so as to efficiently control the strip qualities such as thickness, crown, flatness, and camber. Therefore, a precise prediction model of roll profile is called for in a perfect shape control system. In this paper, a genetic algorithm was applied to improve on the roll profile prediction model in hot strip rolling. In this approach, the optimal design problem is formulated on the basis of a numerical model so as to cover the diverse design variables and objective functions. A genetic algorithm was adopted for conducting design iteration for optimization to determine the coefficient of the numerical model for minimization of errors in the result of the calculated value and the measured data. A comparative analysis showed a satisfactory conformity between them..

• Journal of Korean Academy of Nursing
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• v.41 no.3
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• pp.344-353
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• 2011

Purpose: The purpose of this study was to compare the quality of sleep with the serum lipid profile in patients who have restless legs syndrome (RLS). Methods: The data were obtained from 116 patients with RLS through questionnaires and blood sampling. Results: The results of this study showed correlations between lower quality of sleep and serum lipid profile (LDL Cholesterol) in patients with RLS (r=.19, p=.040). There were correlations for scores of quality of sleep from the, Pittsburgh Sleep Quality Index (PSQI) sub-region between lower subjective sleep quality and serum lipid profile (LDL Cholesterol) (r=.20, p=.026), between fewer hours of sleep duration and serum lipid profile (Total Cholesterol) (r=-.21, p=.024), and, between higher daytime dysfunction and serum lipid profile (LDL Cholesterol) (r=.42, p<.001) of patients with RLS. Conclusion: Pati-ents with RLS have sleep disorders with lower quality of sleep and changes in the serum lipid profile for total cholesterol and LDL cholesterol. That is, patients with RLS have lower quality of sleep and dyslipidemia compared to persons without RLS. Further research is needed to monitor serum the lipid profile in early stage symptoms of midlife adult patients with RLS and especially older women.

• Transactions of Materials Processing
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• v.16 no.4 s.94
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• pp.250-253
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• 2007

In hot strip rolling, the work roll profile is one of the main factors in predicting and correcting the strip profile. Various studies concerning the wear profile and the thermal crown of work roll have been performed, and the results of these studies have shown that the work roll profile must be predicted accurately so as to efficiently control the strip qualities such as thickness, crown, flatness, and camber. Therefore, a precise prediction model of roll profile is called for in a perfect shape control system. In this paper, a genetic algorithm was applied to improve on the roll profile prediction model in hot strip rolling. In this approach, the optimal design problem is formulated on the basis of a numerical model so as to cover the diverse design variables and objective functions. A genetic algorithm was adopted for conducting design iteration for optimization to determine the coefficient of the numerical model for minimization of errors in the result of the calculated value and the measured data. A comparative analysis showed a satisfactory conformity between them.